Order Imbalances and Market Efficiency: Evidence from the Taiwan Stock Exchange

By Yi-Tsung Lee, Yu-Jane Liu, Richard Roll, Avanidhar Subrahmanyam. Journal of Financial and Quantitative Analysis, 2001

179 citations recorded by the source corpus.

Abstract Data from the Taiwan Stock Exchange identify the originator of each submitted order, and there are no designated dealers or specialists. We study marketable order imbalances, i.e., the net order flow resulting from trades that demand immediacy. We distinguish imbalances by trader type (individuals, domestic institutions, foreign institutions) and by the usual size of each trader's order. Day-to-day persistence in order imbalance is strongest for small foreign institutions and weakest for large individual traders. Such persistence emanates both from splitting orders over time and from herding, and there is little evidence that aggregate price pressures from such persistence last beyond a trading day, indicating that de facto market making is quite effective. We attempt to discern which types of traders are de facto liquidity providers, which are likely to be informed, and which trade for liquidity reasons. The evidence indicates that all trader classes are successful market makers, large domestic institutions conduct the most informed trades, and large individuals are noise or liquidity traders.

Subfields: Accounting, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/cb745087f6c7a83b11fb6651353a2b168c5eece4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

In Economics and Econometrics

In Finance

Citations in the corpus

Individual Investors and Volatility (2011, unknown): “Our analysis is also related to the growing literature about the effect of individual investors on price movements (e.g., Lee et al.(2004), Andrade et al.(2008), Barber et al.(2006), Hvidkjaer (2008), Kaniel et al. (2008)).”
Trading imbalances, predictable reversals, and cross-stock price pressure (2008, unknown): “We use a conditional double-sort procedure to test whether trading imbalances predict reversals beyond what is predicted using only returns—see Jagadeesh (1990) and Lehmann (1990).”
Identifying Expensive Trades by Monitoring the Limit Order Book (2017, unknown): “Some authors (e.g. Chordia et al. (2002), Chordia and Subrahmanyam (2004) and Lee et al. (2004)) show that order flow imbalance can be used to predict returns.”
Tracking Retail Investor Activity (2020, methodology): “In this section, we examine the predictive power of our order imbalance measures using Fama-MacBeth regressions as follows: 10 Lee et al. (2004) also find a mixed pattern of contrarian and momentum, using the overall market order imbalance.”